We design Spotify’s consumer experience—end to end, moment to moment, across every screen, platform, and partner integration. Our mission is to make listening feel effortless, personal, and joyful for billions of users around the world. That means turning complexity into clarity across hundreds of touchpoints—from our mobile and desktop apps to the smart speakers, TVs, cars, and integrations where Spotify shows up every day. If it touches a consumer, we shape it. We bring deep insight into human behavior, design, and technology to craft experiences that feel intuitive, expressive, and unmistakably Spotify.
The Policy & Safety team sits within Content Platform in the Experience Mission, building the systems that keep Spotify safe, compliant, and trusted by millions of users and creators. This team owns Spotify’s content moderation infrastructure — from detection models to policy enforcement systems and compliance data pipelines.
Working at the intersection of machine learning, platform engineering, and regulatory compliance, the team partners closely with Trust & Safety, Legal, and Public Affairs. They’re on the critical path for every new content type and social feature — including messaging, comments, and collaborative experiences — ensuring safety is built in from day one. With a strong focus on “safety by default,” the team is investing in large-scale rearchitecture and ML-driven systems to proactively protect users and empower safer interactions across the platform.
What You'll Do
Design, build, and ship production-grade machine learning systems that power content safety and policy enforcement at Spotify scale
Own and lead key technical initiatives across detection, classification, and policy evaluation systems
Develop and maintain ML models for content moderation, including multimodal and LLM-based systems
Build robust evaluation frameworks, including standardized datasets, offline and online metrics, and continuous improvement loops
Drive experimentation to improve model performance, reliability, and fairness in safety-critical systems
Collaborate closely with cross-functional partners in Trust & Safety, Legal, and Public Affairs to align on policy and enforcement needs
Provide technical leadership within the team, mentoring engineers and contributing to ML strategy and prioritization
Represent technical decisions and trade-offs in stakeholder discussions and influence product direction
Who You Are
You have solid experience building and deploying machine learning systems in production environments at scale
You are experienced with training, evaluating, and maintaining ML models using modern frameworks such as PyTorch
You have a deep understanding of machine learning evaluation, including dataset design, metrics, and continuous improvement systems
You know how to design systems that balance performance, reliability, and real-world impact in high-stakes domains
You care about building safe, responsible, and user-centric ML systems
You are comfortable working across disciplines, partnering with legal, policy, and product stakeholders
You have experience leading technical projects and influencing direction within a team or product area
You have experience with distributed systems or backend technologies (e.g., Scala)
Where You'll Be
This role is based in London or Stockholm
We offer you the flexibility to work where you work best! There will be some in person meetings, but still allows for flexibility to work from home.
Skills Required
- Experience building and deploying machine learning systems in production environments at scale
- Experience with training, evaluating and maintaining ML models using modern frameworks such as PyTorch
- Understanding of machine learning evaluation, including dataset design and metrics
- Experience leading technical projects and influencing direction within a team
- Experience with distributed systems or backend technologies like Scala
Spotify Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Spotify and has not been reviewed or approved by Spotify.
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Flexible Benefits — Employees consistently praise the total compensation package beyond base salary, highlighting a mix of RSUs, cash incentives, and stipends alongside core pay. The package is described as flexible and customizable through equity choices (e.g., RSUs, options, cash) that can be tailored for long-term wealth building.
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Leave & Time Off Breadth — Time-off offerings are repeatedly highlighted as substantial, including generous vacation, paid sick days, volunteer time, and flexible holidays. These policies are framed as a meaningful part of the overall rewards experience beyond salary.
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Healthcare Strength — Health coverage is portrayed as comprehensive, spanning medical, dental, vision, life insurance, disability coverage, and mental health support. Additional employer contributions to HSAs are cited as strengthening the overall health and wellness value proposition.
Spotify Insights
What We Do
Spotify transformed music listening forever when it launched in Sweden in 2008. Discover, manage and share over 50m tracks for free, or upgrade to Spotify Premium to access exclusive features including offline mode, improved sound quality, and an ad-free music listening experience.
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